{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:35:59Z","timestamp":1750221359054,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T00:00:00Z","timestamp":1546473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,1,3]]},"DOI":"10.1145\/3297001.3297029","type":"proceedings-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T21:45:18Z","timestamp":1547847918000},"page":"217-223","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Fast Online 'Next Best Offers' using Deep Learning"],"prefix":"10.1145","author":[{"given":"Rekha","family":"Singhal","sequence":"first","affiliation":[{"name":"TCS Research, Mumbai"}]},{"given":"Gautam","family":"Shroff","sequence":"additional","affiliation":[{"name":"TCS Research, Delhi"}]},{"given":"Mukund","family":"Kumar","sequence":"additional","affiliation":[{"name":"TCS Research, Mumbai"}]},{"given":"Sharod Roy","family":"Choudhury","sequence":"additional","affiliation":[{"name":"TCS Research, Mumbai"}]},{"given":"Sanket","family":"Kadarkar","sequence":"additional","affiliation":[{"name":"TCS Research, Mumbai"}]},{"given":"Rupinder","family":"Virk","sequence":"additional","affiliation":[{"name":"TCS Research, Mumbai"}]},{"given":"Siddharth","family":"Verma","sequence":"additional","affiliation":[{"name":"TCS Research, Delhi"}]},{"given":"Vartika","family":"Tewari","sequence":"additional","affiliation":[{"name":"TCS Research, Delhi"}]}],"member":"320","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"https:\/\/hadoop.apache.org\/docs\/r1.2.1\/hdfs_design.html. (Aug","author":"HDFS.","year":"2013","unstructured":"2013. HDFS. https:\/\/hadoop.apache.org\/docs\/r1.2.1\/hdfs_design.html. (Aug 2013 ). {Online; accessed 27. Feb. 2018}. 2013. HDFS. https:\/\/hadoop.apache.org\/docs\/r1.2.1\/hdfs_design.html. (Aug 2013). {Online; accessed 27. Feb. 2018}."},{"key":"e_1_3_2_1_2_1","unstructured":"2016. Seldon: Open source recommendation system. http:\/\/docs.seldon.io\/benchmark-recommendation.html. (2016).  2016. Seldon: Open source recommendation system. http:\/\/docs.seldon.io\/benchmark-recommendation.html. (2016)."},{"key":"e_1_3_2_1_3_1","volume-title":"https:\/\/www.json.org. (Oct","author":"JSON.","year":"2017","unstructured":"2017. JSON. https:\/\/www.json.org. (Oct 2017 ). {Online; accessed 27. Feb. 2018}. 2017. JSON. https:\/\/www.json.org. (Oct 2017). {Online; accessed 27. Feb. 2018}."},{"key":"e_1_3_2_1_4_1","unstructured":"2017. Kaggle Instacart Challenge. https:\/\/www.kaggle.com\/c\/instacart-market-basket-analysis. (2017).  2017. Kaggle Instacart Challenge. https:\/\/www.kaggle.com\/c\/instacart-market-basket-analysis. (2017)."},{"key":"e_1_3_2_1_5_1","unstructured":"2017. List of Open Source Recommendation Systems. https:\/\/github.com\/grahamjenson\/list_of_recommender_systems. (2017).  2017. List of Open Source Recommendation Systems. https:\/\/github.com\/grahamjenson\/list_of_recommender_systems. (2017)."},{"key":"e_1_3_2_1_6_1","unstructured":"2017. PAKDD Recobell Challenge. http:\/\/www.recobell.com\/rb\/main.php?menu=pakdd2017. (2017).  2017. PAKDD Recobell Challenge. http:\/\/www.recobell.com\/rb\/main.php?menu=pakdd2017. (2017)."},{"volume-title":"Apache Ignite - Open source memory-centric distributed database, caching, and processing platform. https:\/\/ignite.apache.org. (Feb","year":"2018","key":"e_1_3_2_1_7_1","unstructured":"2018. Apache Ignite - Open source memory-centric distributed database, caching, and processing platform. https:\/\/ignite.apache.org. (Feb 2018 ). {Online; accessed 27. Feb. 2018}. 2018. Apache Ignite - Open source memory-centric distributed database, caching, and processing platform. https:\/\/ignite.apache.org. (Feb 2018). {Online; accessed 27. Feb. 2018}."},{"volume-title":"Apache Kafka - A distributed streaming platform. https:\/\/kafka.apache.org. (Feb","year":"2018","key":"e_1_3_2_1_8_1","unstructured":"2018. Apache Kafka - A distributed streaming platform. https:\/\/kafka.apache.org. (Feb 2018 ). {Online; accessed 27. Feb. 2018}. 2018. Apache Kafka - A distributed streaming platform. https:\/\/kafka.apache.org. (Feb 2018). {Online; accessed 27. Feb. 2018}."},{"volume-title":"Apache Spark\u2122 - Lightning-Fast Cluster Computing. https:\/\/spark.apache.org. (Jan","year":"2018","key":"e_1_3_2_1_9_1","unstructured":"2018. Apache Spark\u2122 - Lightning-Fast Cluster Computing. https:\/\/spark.apache.org. (Jan 2018 ). {Online; accessed 27. Feb. 2018}. 2018. Apache Spark\u2122 - Lightning-Fast Cluster Computing. https:\/\/spark.apache.org. (Jan 2018). {Online; accessed 27. Feb. 2018}."},{"key":"e_1_3_2_1_10_1","unstructured":"2018. Flask web development framework. http:\/\/flask.pocoo.org\/. (Jan 2018). {Online; accessed 27. Feb. 2018}.  2018. Flask web development framework. http:\/\/flask.pocoo.org\/. (Jan 2018). {Online; accessed 27. Feb. 2018}."},{"volume-title":"MongoDB for GIANT Ideas. https:\/\/www.mongodb.com. (Feb","year":"2018","key":"e_1_3_2_1_11_1","unstructured":"2018. MongoDB for GIANT Ideas. https:\/\/www.mongodb.com. (Feb 2018 ). {Online; accessed 27. Feb. 2018}. 2018. MongoDB for GIANT Ideas. https:\/\/www.mongodb.com. (Feb 2018). {Online; accessed 27. Feb. 2018}."},{"key":"e_1_3_2_1_12_1","unstructured":"2018. Tornado Web Server. http:\/\/www.tornadoweb.org\/en\/stable. (Jan 2018). {Online; accessed 27. Feb. 2018}.  2018. Tornado Web Server. http:\/\/www.tornadoweb.org\/en\/stable. (Jan 2018). {Online; accessed 27. Feb. 2018}."},{"volume-title":"Web Frameworks for Python. https:\/\/wiki.python.org\/moin\/WebFrameworks. (Jan","year":"2018","key":"e_1_3_2_1_13_1","unstructured":"2018. Web Frameworks for Python. https:\/\/wiki.python.org\/moin\/WebFrameworks. (Jan 2018 ). {Online; accessed 27. Feb. 2018}. 2018. Web Frameworks for Python. https:\/\/wiki.python.org\/moin\/WebFrameworks. (Jan 2018). {Online; accessed 27. Feb. 2018}."},{"volume-title":"Welcome to Spark Python API Docs. https:\/\/spark.apache.org\/docs\/latest\/api\/python\/. (Jan","year":"2018","key":"e_1_3_2_1_14_1","unstructured":"2018. Welcome to Spark Python API Docs. https:\/\/spark.apache.org\/docs\/latest\/api\/python\/. (Jan 2018 ). {Online; accessed 27. Feb. 2018}. 2018. Welcome to Spark Python API Docs. https:\/\/spark.apache.org\/docs\/latest\/api\/python\/. (Jan 2018). {Online; accessed 27. Feb. 2018}."},{"key":"e_1_3_2_1_15_1","unstructured":"2018. XGBoost - Extreme Gradient Boosting. http:\/\/xgboost.readthedocs.io\/en\/latest\/model.html. (Feb 2018). {Online; accessed 27. Feb. 2018}.  2018. XGBoost - Extreme Gradient Boosting. http:\/\/xgboost.readthedocs.io\/en\/latest\/model.html. (Feb 2018). {Online; accessed 27. Feb. 2018}."},{"key":"e_1_3_2_1_16_1","volume-title":"A Reliable Effective Terascale Linear Learning System. CoRR abs\/1110.4198","author":"Agarwal Alekh","year":"2011","unstructured":"Alekh Agarwal , Olivier Chapelle , Miroslav Dud\u00edk , and John Langford . 2011. A Reliable Effective Terascale Linear Learning System. CoRR abs\/1110.4198 ( 2011 ). arXiv:1110.4198 http:\/\/arxiv.org\/abs\/1110.4198 Alekh Agarwal, Olivier Chapelle, Miroslav Dud\u00edk, and John Langford. 2011. A Reliable Effective Terascale Linear Learning System. CoRR abs\/1110.4198 (2011). arXiv:1110.4198 http:\/\/arxiv.org\/abs\/1110.4198"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/3157794.3157797"},{"key":"e_1_3_2_1_18_1","volume-title":"Jblas: Linear Algebra for Java","author":"Braun Mikio L.","year":"2015","unstructured":"Mikio L. Braun , Johannes Schaback , Matthias L. Jugel , Nicolas Oury , 2015 . Jblas: Linear Algebra for Java . http:\/\/jblas.org. (May 2015). {Online; accessed 27. Feb. 2018}. Mikio L. Braun, Johannes Schaback, Matthias L. Jugel, Nicolas Oury, et al. 2015. Jblas: Linear Algebra for Java. http:\/\/jblas.org. (May 2015). {Online; accessed 27. Feb. 2018}."},{"key":"e_1_3_2_1_19_1","volume-title":"Pranav Jindal and Jure Leskovec","author":"Liu Yuchen Liu Rahul Jerry Zitao","year":"2017","unstructured":"Jerry Zitao Liu Yuchen Liu Rahul Sharma Charles Sugnet Mark Ulrichm Chantat Eksombatchai , Pranav Jindal and Jure Leskovec . 2017 . Pixie : A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time . https:\/\/arxiv.org\/abs\/1711.07601v1. (November 2017). Jerry Zitao Liu Yuchen Liu Rahul Sharma Charles Sugnet Mark Ulrichm Chantat Eksombatchai, Pranav Jindal and Jure Leskovec. 2017. Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time. https:\/\/arxiv.org\/abs\/1711.07601v1. (November 2017)."},{"key":"e_1_3_2_1_20_1","volume-title":"Aur\u00c3l'lien Bibaut.","author":"Mark","year":"2017","unstructured":"Mark J. van der Laan Cheng Ju , Aur\u00c3l'lien Bibaut. 2017 . The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification . https:\/\/arxiv.org\/abs\/1704.01664. (2017). Mark J. van der Laan Cheng Ju, Aur\u00c3l'lien Bibaut. 2017. The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification. https:\/\/arxiv.org\/abs\/1704.01664. (2017)."},{"volume-title":"IEEE 8th International Conference for Internet Technology and Secured Transactions (ICITST).","author":"Chongxiao Cao Fengguang Song","key":"e_1_3_2_1_21_1","unstructured":"Fengguang Song Chongxiao Cao and Daniel G. Waddington . 2013. Implementing a high-performance recommendation system using Phoenix++ . In IEEE 8th International Conference for Internet Technology and Secured Transactions (ICITST). Fengguang Song Chongxiao Cao and Daniel G. Waddington. 2013. Implementing a high-performance recommendation system using Phoenix++. In IEEE 8th International Conference for Internet Technology and Secured Transactions (ICITST)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864770"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1561\/1100000009"},{"key":"e_1_3_2_1_24_1","volume-title":"Levent Koc and Hemal Shah","author":"Tal Shaked Tushar Chandra Jeremiah Harmsen","year":"2016","unstructured":"Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir Rohan Anil Zakaria Haque Lichan Hong Vihan Jain Xiaobing Liu Heng-Tze Cheng , Levent Koc and Hemal Shah . 2016 . Wide and Deep Learning for Recommender Systems . http:\/\/arxiv.org\/pdf\/1606.07792.pdf. (June 2016). Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir Rohan Anil Zakaria Haque Lichan Hong Vihan Jain Xiaobing Liu Heng-Tze Cheng, Levent Koc and Hemal Shah. 2016. Wide and Deep Learning for Recommender Systems. http:\/\/arxiv.org\/pdf\/1606.07792.pdf. (June 2016)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.05.043"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/WI.2016.0072"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402729"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"H. Salehinejad and S. Rahnamayan. 2016. Customer shopping pattern prediction: A recurrent neural network approach. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 1--6.  H. Salehinejad and S. Rahnamayan. 2016. Customer shopping pattern prediction: A recurrent neural network approach. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 1--6.","DOI":"10.1109\/SSCI.2016.7849921"},{"volume-title":"Towards Using Cached Data Mining for Large Scale Recommender Systems","author":"Sheth Swapneel","key":"e_1_3_2_1_30_1","unstructured":"Swapneel Sheth and Gail Kaiser . 2013. Towards Using Cached Data Mining for Large Scale Recommender Systems . Springer Berlin Heidelberg , Berlin, Heidelberg , 349--357. Swapneel Sheth and Gail Kaiser. 2013. Towards Using Cached Data Mining for Large Scale Recommender Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, 349--357."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007263.3007276"},{"key":"e_1_3_2_1_32_1","volume-title":"Working Notes of the 6th International Conference of the CLEF Initiative. CEUR Workshop Proceedings, 10","author":"Verbitskiy Ilya","year":"2015","unstructured":"Ilya Verbitskiy , Patrick Probst , and Andreas Lommatzsch . 2015 . Development and Evaluation of a Highly Scalable News Recommender System . In Working Notes of the 6th International Conference of the CLEF Initiative. CEUR Workshop Proceedings, 10 . http:\/\/ceur-ws.org\/Vol-1391\/149-CR.pdf Vol-1391, urn:nbn:de:0074-1391-8. Ilya Verbitskiy, Patrick Probst, and Andreas Lommatzsch. 2015. Development and Evaluation of a Highly Scalable News Recommender System. In Working Notes of the 6th International Conference of the CLEF Initiative. CEUR Workshop Proceedings, 10. http:\/\/ceur-ws.org\/Vol-1391\/149-CR.pdf Vol-1391, urn:nbn:de:0074-1391-8."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/2893873.2894086"}],"event":{"name":"CoDS-COMAD '19: 6th ACM IKDD CoDS and 24th COMAD","acronym":"CoDS-COMAD '19","location":"Kolkata India"},"container-title":["Proceedings of the ACM India Joint International Conference on Data Science and Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3297001.3297029","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3297001.3297029","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:26:14Z","timestamp":1750213574000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3297001.3297029"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,3]]},"references-count":33,"alternative-id":["10.1145\/3297001.3297029","10.1145\/3297001"],"URL":"https:\/\/doi.org\/10.1145\/3297001.3297029","relation":{},"subject":[],"published":{"date-parts":[[2019,1,3]]},"assertion":[{"value":"2019-01-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}